Personalizing content based on mood

ABSTRACT

In order to increase the efficacy of a mood-based playlisting system, a mood sensor such as a camera may be used to provide mood information to the mood model. When the mood sensor includes a camera, a camera may be used to capture an image of the user. The image is analyzed to determine a mood for the user so that content may be selected responsive to the mood of the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation of U.S. application Ser. No. 12/827,788, filedJun. 30, 2010 (now allowed), which is a continuation of U.S. applicationSer. No. 11/025,881, filed Dec. 30, 2004 (now U.S. Pat. No. 7,764,311),which is a continuation-in-part of U.S. application Ser. No. 10/448,469,filed May 30, 2003 (now abandoned), and U.S. application Ser. No.10/448,316, filed May 30, 2003 (now U.S. Pat. No. 7,243,104). The entirecontents of the above-referenced applications are expressly incorporatedherein by reference.

TECHNICAL FIELD

Digital content may represent movies, music, slides, games and otherforms of electronic content. With the maturation of local area and widearea networking technologies, digital content may be distributed on awide variety of devices and a wide variety of formats.

BACKGROUND

Digital content is distributed on a wide variety of devices and in awide variety of formats. The digital content may include movies, music,slides, games and other forms of electronic content.

SUMMARY

In one general sense, a computer program is configured to manage contentaccessed by a user by using a camera to capture an image of the user,analyzing the image to determine an actual mood for the user,identifying a desired mood state, comparing the actual mood to thedesired mood state, and based on results of the comparison, selectingcontent for the user.

Implementations may include one or more of the following. For example,analyzing the image to determine the actual mood for the user mayinclude analyzing one or more facial expressions for the user. Analyzingthe facial expressions may include determining if a brow is furrowed toindicate an angry mood, an upset mood, or an intensity of a mood,determining if the brow is unfurrowed to indicate a neutral mood, acontent mood, or a mood of lesser intensity, or analyzing a user's mouthfeatures to determine if the user is smiling, frowning, angry,experiencing an intense mood, or experiencing a neutral mood.

Analyzing the facial expression may include identifying a change in alocation of a facial feature, and using the change in the location todetermine the actual mood. Analyzing the change in the location of thefacial feature includes determining that a brow, a wrinkle, a dimple, ahairline, an ear, a nose, a mouth, or a chin is located in a differentlocation. Analyzing the image to determine the actual mood may includescomparing current information related to a current image with previousinformation related to a previous image, identifying a differentialdescribing how the current information differs from the previousinformation, and using the differential to determine the actual mood.

Analyzing the image to determine the actual mood may include identifyinga level of user movement, and using the user movement to determine themood. Identifying the level of user movement may include identifying arhythmic motion, and using the user movement to determine the moodincludes using the rhythmic motion to identify the actual mood.

It may be determined that selected content did not transition the userto the desired mood, and a user profile may be adjusted to reflect avariation between the predicted mood and the actual mood.

The user may be enrolled in a training regimen to determine a userprofile for the user, and the user profile may be used to determine theactual mood for the user.

Enrolling the user in the training regimen may include associating oneor more facial expressions of the user with one or more moods. Enrollingthe user in the training regimen may include evaluating a user responseto one or more selections of content. Evaluating the user response tothe one or more selections of content may include generating perceivableoutput for a first content selection, presenting an image of the usercaptured in response to the generation of the perceivable output, andprompting the user to specify the actual mood as depicted in the image.

The user may be enabled to identify facial features indicative of theactual mood. Evaluating the user response to the one or more selectionsof content may include generating perceivable output for a first contentselection, and prompting the user to specify the actual mood. Enrollingthe user in the training regimen may include prompting the user toconfirm that a mood determined by evaluating an image relates to anactual mood. Enrolling the user in the training regimen may includegenerating perceivable output from a content selection configured totest the user's response, using the image to evaluate a user's response,generating a mood model using the user's response, and selecting contentusing the mood model. The operations of generating additionalperceivable output from the mood impulse content selection and using theimage to evaluate the mood impulse response may be repeated to generatea comprehensive mood impulse response function such that additional moodinformation for different moods is gleaned.

In another general sense, a computer program may be configured to managecontent accessed by a user by identifying an initial mood of a user,identify a mood destination for a user, using a mood-based playlistingsystem to select content to be made perceivable to the user to inspire atransition from the initial mood to inspire a transition from theinitial mood to the mood destination, rendering the selected content tothe user, using a camera to capture an image of the user reacting toperception of the content, analyzing the image to determine an actualmood for the user, comparing the mood destination to the actual mood,and based on results of the comparison, selecting content for the user.

Implementations may include the following or other features. Forexample, comparing the mood destination to the actual mood may includedetermining a different between the initial mood and the actual mood,and using the difference to determine content needed to inspire furthertransition to the mood destination.

In yet another general sense, a computer program may be configured tomanage content accessed by a user by using a mood monitor to capturemood signals from the user, analyzing the mood signals to determine amood for the user, and responsive to the mood, selecting content for theuser.

Implementations may include the following or other features. Forexample, using the mood monitor to capture mood signals may includeusing an electrical signal monitor, a brain wave monitor, a brainimaging device, a microphone, an electromagnetic antenna, or anolfactory sensor to capture the mood signals.

Other features will be apparent from the description and drawings, andfrom the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a communications system that enableselectronic content to be distributed.

FIG. 2A is a graph of a mood spectrum that illustrates how a selectionof content may be scored to quantify the mood in an automated manner.

FIG. 2B is a graph illustrating how a mood spectrum and scoring systemmay be used to associate content with an actual mood.

FIG. 2C is a graph illustrating a three-dimensional mood managementsystem that illustrates how mood consistency may be maintained usingthree or more factors.

FIG. 2D is a graph illustrating how mood transitions may incorporateintermediate tracks to create a more successful transition in reaching amood destination.

FIG. 3 is a block diagram of a mood-based playlisting system.

FIG. 4 is a flow chart showing how mood consistency may be maintainedbetween two tracks.

FIG. 5 is a flow chart showing how mood consistency may be maintainedusing a three-dimensional model to determine mood consistency.

FIG. 6 is a flow chart showing how a playlist of content may betransitioned from a mood originating point to a mood destination usingintermediate tracks.

FIG. 7 is an exemplary graphical user interface (GUI) that may bepresented to a user accessing a mood-based playlisting system.

FIGS. 8, 9, and 10 illustrate exemplary GUIs that may be presented to auser in training a mood recognition engine used in a mood-basedplaylisting system.

FIG. 11 is an exemplary listing of variables illustrating how facialcomponents may be used to generate a mood.

FIGS. 12A and 12B illustrate an exemplary user profile for a screen namethat relates a component in a facial expression to a mood that may beused in a mood-based playlisting system.

FIGS. 13A, 13B, and 13C illustrate an exemplary scripted sequence ofoperations for a mood-based playlisting system configured to providecontent using mood-based selection criteria based in part on a camera toprovide mood information.

FIG. 14 is a flow chart of an exemplary process by which a user's moodvariations may be recorded.

FIG. 15 is an exemplary GUI that illustrates how different inputs may beused as a mood sensor into a mood-based playlisting system.

DETAILED DESCRIPTION

Digital content such as digitally encoded songs (e.g., MP3 and NW files)and video may be accessed on a variety of platforms through a variety ofdistribution channels. Examples of the platforms include personalcomputers, specialized appliances (e.g., a compact digital audio playersuch as Apple's iPod™), home stereo systems, and other devices. Examplesof the distribution channels include Internet radio and televisionstations, network-based on demand services, Internet and retail outletpurchasing, and promotional distribution (e.g., an optical disk providedin a magazine).

The plethora of digital content, distribution channels and contentproviders make it very easy for a user to identify and select adifferent content sources that are more responsive to the user'sparticular interest at a particular time. For instance, to selectcontent responsive to the user's particular interest at a particulartime, a user's mood or emotional zone may be mathematically modeled. Amedia player (e.g., a jukebox or an application on a personal computer)may be used to select content responsive to the determined mood. Themood-based playlisting system may be used to select or plan a sequenceof tracks (e.g., a playlist) to achieve or preserve a desired mood-statefor the user. A mood transition may be planned so that any mood changebetween different ‘tracks’ comports with a determined mood transitionspecification.

A mood state may be modeled using mood information for a contentselection (e.g., a digitally-encoded song) and/or by using moodinformation determined by how a user is interacting with the mediaplayer. For example, a playlist engine on a host may determine that aparticular song is associated with an uplifting mood, andcorrespondingly may select additional songs and advertisementsconsistent with the uplifting mood. However, a likelihood exists that anactual mood for a user may differ from the predicted mood for a user.For instance, a user may be listening to classical music as the user isfrantically packing last-minute for a vacation. The output of classicalmusic may indicate a relaxed mood for the predicted mood, but the actualmood may differ, as the user is anxious, stressed, and/or panicked withlast-minute vacation preparations.

In order to increase the efficacy of a mood-based playlisting system, amood sensor such as a camera may be used to provide mood information tothe mood model. When the mood sensor includes a camera, a camera may beused to capture an image of the user. The image is analyzed to determinea mood for the user so that content may be selected responsive to themood of the user.

For example, a user listens to an interest radio station. The user mayinitially select one of several Internet radio stations before settlingon a 1980s-oriented station. The personal computer may include a desktopvideo camera that captures imagery of the user in front of the personalcomputer.

The 1980s-oriented station is flexibly configured to enable access todifferent content based on the mood of the user. Thus, if a user doesnot ‘like’ a first selection (e.g., WHAM's Wake Me Up), the user mayadvance to another selection, either explicitly (e.g., by selecting anext-track feature) or implicitly (e.g., by determining that the user isin a different mood). In particular, the media player or a host accessedby the media player may analyze imagery provided by the desktop videocamera and determine a mood for the user. When the user's facialexpression indicates disgust, anger, or upset moods, the media playermay select different content, for example, by selecting a differenttrack at the conclusion of the first selection, or by advancing toanother song altogether in the middle of the song.

In one implementation, the Internet radio station places anadvertisement responsive to a mood state, or controls the mood state ofprevious songs to place an advertisement. For example, no Internet radiostation may precede a desired advertisement with a specified sequence ofone or more content selections that foster the desired mood. A cameramay be used to evaluate whether the desired or predicted mood along thepreceding sequence represents an actual mood for the user. When theactual mood differs from the predicted mood, the media player may selecta different track or sequence of tracks to foster the desired mood orselects a different advertisement.

FIG. 1 illustrates a media-based communications system 100 that maydistribute content electronically. The media-based communications system100 includes a content source 110, a network 120, and a player 130.Although the media-based communications system 100 is shown as anetwork-based system, the media-based playlisting system may accessmedia files residing in a standalone device or in a differentconfiguration. For example, a mobile jukebox may play content in theform of music encoded in a media file format.

The content source 110 generally includes one or more devices configuredto distribute digital content. For example, as shown, the content source110 includes a server 112 and a duplicating switch 114.

Typically, a content source 110 includes a collection or library ofcontent for distribution. Alternatively, or in addition, the contentsource may convert a media source (e.g., a video or audio teed) into afirst feed of data units for transmission across the network 120. Thecontent source 110 may include a general-purpose computer having acentral processor unit (CPU), and memory/storage devices that store dataand various programs such as an operating system and one or moreapplication programs. Other examples of a content source 110 include aworkstation, a server 112, a special purpose device or component, abroadcast system, other equipment, or some combination thereof capableof responding to and executing instructions in a defined manner. Thecontent source 110 also may include an input/output (I/O) device (e.g.,video and audio input and conversion capability), and peripheralequipment such as a communications card or device (e.g., a modem or anetwork adapter) for exchanging data with the network 120.

The content source 110 includes playlisting software configured tomanage the distribution of content. The playlisting software organizesor enables access to content by a user community. For example, thecontent source 110 may be operated by an Internet radio station that issupporting a user community by streaming an audio signal, and mayarrange a sequence of songs accessed by the user community.

The playlisting software includes mood-based playlisting software thatmaintains a consistent mood in selecting content. Generally, themood-based playlisting software selects content so that any related moodtransition between different content components is acceptable.

The content source includes a duplicating switch 114. Generally, aduplicating switch 114 includes a device that performs networkoperations and functions in hardware (e.g., in a chip or part of chip).In some implementations, the duplicating switch may include an ASIC(“Application Specific Integrated Circuit”) implementing networkoperations logic directly on a chip (e.g., logical gates fabricated on asilicon wafer and then manufactured into a chip). For example, an ASICchip may perform filtering by receiving a packet, examining the IPaddress of the received packet, and filtering based on the IP address byimplementing a logical gate structure in silicon.

Implementations of the device included in the duplicating switch mayemploy a Field Programmable Gate Array (FPGA). A FPGA is generallydefined as including a chip or chips fabricated to allow a third partydesigner to implement a variety of logical designs on the chip. Forexample, a third party designer may load a FPGA with a design to replacethe received IP addresses with different IP addresses, or may load theFPGA with a design to segment and reassemble IP packets as they aremodified while being transmitted through different networks.

Implementations of the device included in the duplicating switch alsomay employ a network processor. A network processor is generally definedto include a chip or chips that allow software to specify which networkoperations will be performed. A network processor may perform a varietyof operations. One example of a network processor may include severalinterconnected RISC (“Reduced Instruction Set Compute?”) processorsfabricated in a network processor chip. The network processor chip mayimplement software to change an IP address of an IP packet on some ofthe RISC processors. Other RISC processors in the network processor mayimplement software that monitors which terminals are receiving an IPstream.

Although various examples of network operations were defined withrespect to the different devices, each of the devices tends to beprogrammable and capable of performing the operations of the otherdevices. For example, the FPGA device is described as the device used toreplace IP addresses and segment and reassemble packets. However, anetwork processor and ASIC are generally capable of performing the sameoperations.

The network 120 may include hardware and/or software capable of enablingdirect or indirect communications between the content source 110 and theplayer 130. As such, the network 120 may include a direct link betweenthe content source and the player, or it may include one or morenetworks or subnetworks between them (not shown). Each network orsubnetwork may include, for example, a wired or wireless data pathwaycapable of carrying and receiving data. Examples of the delivery networkinclude the Internet, the World Wide Web, a WAN (“Wide Area Network”), aLAN (“Local Area Network”), analog or digital wired and wirelesstelephone networks, radio, television, cable, satellite, and/or anyother delivery mechanism for carrying data.

The player 130 may include one or more devices capable of accessingcontent on the content source 110. The player 130 may include acontroller (not shown) that processes instructions received from orgenerated by a software application, a program, a piece of code, adevice, a computer, a computer system, or a combination thereof, whichindependently or collectively direct operations of the player 130. Theinstructions may be embodied permanently or temporarily in any type ofmachine, component, equipment, storage medium, or propagated signal thatis capable of being delivered to the player 130 or that may reside withthe controller at player 130. Player 130 may include a general-purposecomputer (e.g., a personal computer (PC) 132) capable of responding toand executing instructions in a defined manner, a workstation, anotebook computer, a PDA (“Personal Digital Assistant”) 134, a wirelessphone 136, a component, other equipment, or some combination of theseitems that is capable of responding to and executing instructions.

In one implementation, the player 130 includes one or more informationretrieval software applications (e.g., a browser, a mail application, aninstant messaging client, an Internet service provider client, or an AOLTV or other integrated client) capable of receiving one or more dataunits. The information retrieval applications may run on ageneral-purpose operating system and a hardware platform that includes ageneral-purpose processor and specialized hardware for graphics,communications and/or other capabilities. In another implementation,player 130 may include a wireless telephone running a micro-browserapplication on a reduced operating system with general purpose andspecialized hardware capable of operating in mobile environments.

The player 130 may include one or more media applications. For example,the player 130 may include a software application that enables theplayer 130 to receive and display an audio or video data stream. Themedia applications may include controls that enable a user to configurethe user's media environment. For example, if the media application isreceiving an Internet radio station, the media application may includecontrols that enable the user to select an Internet radio station, forexample, through the use of “preset” icons indicating the station genre(e.g., country) or a favorite. In another example, the controls mayenable the user to rewind or fast-forward a received media stream. Forexample, if a user does not care for a track on a particular station,the user may interface with a “next track” control that will queue upanother track (e.g., another song).

The media application includes mood-based playlisting software. Themood-based playlisting software may work independently of, or inconjunction with, playlisting software residing on the content source110. The mood-based playlisting software may mitigate the moodtransition created when the content changes. In one example, theplaylisting software permits the user to select from a recommended listof content that is consistent with the previous or present track. Inanother example, the mood-based playlist software may seamlessly managethe transition of content.

FIGS. 2A-2D describe a mood modeling system that may be used by thesystems described with respect to FIG. 1. FIG. 2A illustrates a moodspectrum 200 that may be used to determine a mood consistency between aselection of content and planned future content. Mood spectrum 200 hasbeen abstracted to be independent of the underlying mood, and has beennormalized in the range from 0 to 10. In mood spectrum 200, the moodindicator 205 for the current track has a value of approximately 5 onthe mood spectrum 200. The mood indicator 205 for the current track isrelated to the mood spectrum 210 consistent with the current track,which indicates mood values for content that may be selected consistentwith the mood value for the current track under consideration. In oneexample, the playlist and content selection is being planned and thecurrent track under consideration has not been distributed. In anotherexample, the current track under consideration has been or is beingdistributed (e.g., across the Internet by an Internet radio station).

FIG. 2B illustrates a graph 220 how content may be categorized using oneor more moods and specifically describes how the mood indicatorassociated with a particular piece of content may span multiple moods.As shown, the moods include “angry,” “excitement,” “dance,” “romantic,”“mellow,” and “sad.” FIG. 2B uses a 1-dimensional axis to categorizecontent along the mood spectrum 225. Specifically, the content in FIG.2B spans two of the moods, specifically, dance and romance. Otherdimensioning systems relating to more than two moods may be used. Forexample, an X dimensional system may gauge X moods across X axes.Nevertheless, regardless of the number of axes that are used, aselection of content may be related to various moods to identify futurecontent that is consistent with the mood of the content that has beenselected.

FIG. 2B includes a mood indicator 230 for the current track. The moodindicator 230 describes a particular mood rating for a piece of contentthat has been identified. The content that has been identified mayinclude a selection of content that is actually being played or one thatis destined for one or more users. Alternatively, the mood indicator fora current track may be used to create a user playlist to better identifydesired content deemed compatible for a user. As is shown in FIG. 2B,the mood indicator 230 for the current track lies within the moodspectrum 225 consistent with the current track. This mood spectrum 225indicates that content that falls within dance and romantic themes isdeemed consistent with the mood indicator for the current track.

In one implementation, the consistency with the current track and theidentification of a particular mood spectrum may be determined byscoring the current track and a proposed next track and determining therelationship between the score for the current track and the score forthe proposed next track. Alternatively, a selection of content may beassociated with one or more discrete values that describe the content.For example, a song may be associated with letters, each of whichdescribes one or more themes that may be used to characterize the song.Thus, as is shown in FIG. 2B, if D and R were used to identify,respectively, dance and romantic themes, a record describing the currenttrack could have a D and a R in its record/metadata.

Referring to FIG. 2C, a three-dimensional mood management graph 240 isshown that illustrates how mood spectrum consistency may be determinedacross three factors, influences, or moods. Specifically, thethree-dimensional coordinate system for the current track 245 is shownwithin a three-dimensional volume describing the mood spectrum boundary250 as a function of three coordinates. Also shown is a first song 255that does not fall within the volume of the mood spectrum boundaries 250and a second song 260 that lies within the mood spectrum boundary 255.Thus, when content is being selected, if the mood spectrum boundary 250is being used as the determining criteria, song 255 may be excluded asit lies outside the mood spectrum boundary 250, while song 260 may beincluded in the playlist as it lies within the mood spectrum boundary250.

Depending on the implementation and the configuration, the mood spectrumboundary may represent a simpler function such as a cone or a sphere.For example, a sphere may be used to identify equidistant points thatfall within a certain mood range of the current track. However, the moodspectrum boundary 250 need not include a simple function. For example,if detailed analytics are used to measure mood spectrum consistency anduser response, a more detailed and non-symmetrical volume may be used tomeasure the mood spectrum boundary 250. One illustration of this mayinclude content that may be very consistent across one axis for multiplethemes, but inconsistent with minor changes across a different axis inmood spectrum. For example, if the content is being scored acrosslyrics, tempo and intensity, lyrics that may contain age-appropriatesuggestions may only be consistent with content that is similarlyappropriate for the identified age. In contrast, content that features aslower tempo may be consistent with music across multiple themes with asimilar tempo. Accordingly, the function that describes the moodspectrum boundary 250 of the current track 240 may incorporate analyticsthat permit a small tolerable deviation in the lyrical deviation whilealso permitting a wider variation in the tempo axis.

FIG. 2D illustrates a graph of a three-dimensional mood consistencyscoring system 270 that illustrates how mood transitions may be plannedso that the mood may be changed from a current mood originating point toa mood destination. The transitions may be structured such that atransition directly from a mood originating point to a mood destinationthat otherwise appears difficult or unsuccessful may be made moresuccessful by using one or more intermediate transitions. Thus, thelikelihood of a successful transition between the mood originating pointand the mood destination point is increased.

Mood scoring system 270 illustrates a mood originating point 275 and amood destination 280. The general mood transition that is required isillustrated by the vector 285 from the mood originating point 275 to themood destination point 280. However, the mood consistency volume 277 formood originating point 275 does not include the mood destination point280. Accordingly, one or more intermediary tracks may be used tosuccessfully transition one or more users to the mood destination point.

To accomplish this transition, intermediary track 290 is used as thenext content selection to create a mood that is closer to the mooddestination point 280, even though the consistency volume 292 for theintermediary track 290 does not actually reach or include the mooddestination 280. After the intermediary track 290 is selected, a secondintermediary track 295 is added to the playlist to move the current moodindicator closer to the mood destination 280. As is shown in FIG. 2D,the intermediary tracks 290 and 295 both lie within the same transitionvolume 292, thus preserving a consistent mood transition from theintermediary track 290 to the intermediary track 295. From theintermediary track 295, the system may transition directly to the mooddestination point 280 and preserve the consistent mood as both theintermediary track 295 and the mood destination point 280 lie within themood transition volume 297.

Although the transition from the mood originating point 275 to the mooddestination point 280 features the use of two intermediary tracks, theimplementation of a successful transition need not be limited to the twointermediary tracks that are shown. For example, depending on theconfiguration, no intermediary tracks may be required to successfullytransition from the mood originating point 275 to the mood destinationpoint 280. Alternatively, one, two, three, or more intermediary tracksmay be used to successfully transition from the mood originating point275 to the mood destination point 280.

The intermediary tracks need not resemble similar forms of content. Forexample, the mood originating point for the current track may include asong that is being transmitted, a first intermediary track may include acommercial, a second intermediary track may include a second song, andthe mood destination point may relate to a planned advertisement thathas been targeted for increased chances of success.

Also, the volumes that describe the mood consistency may be configuredto reflect probabilistic chances of success and may change, based on thedesired chance of success. For example, the mood consistency volume 277may be planned on a model of mood consistency such that transitioningfrom the mood originating point 275 to the intermediary track 290 willpreserve 90% of the audience upon that transition. Alternatively, iffewer intermediary tracks are desired, a larger mood consistency volumethat covers more distance may be used based upon a modeled probabilityof 50%. Thus, in this model, fewer intermediary tracks may be requiredto reach the mood destination point.

Finally, the transitions that are shown may include real-time feedbackto better predict the actual user response to be transitioned. Forexample, a test audience may be sent the intermediary track in advanceof the larger audience. If the response of the test audience indicatesthat the transition is not as successful as was expected, an alternatepath may be plotted to increase the chance that the transition willpreserve the audience. For example, an intermediary track may be chosenthat lies closer to the mood originating point. Another example of analternative path that may be chosen includes a trusted transition thathas been used previously and is associated with what is believed to be ahigher success rate in transitioning.

FIG. 3 illustrates a mood-based playlisting system 300 that may be usedto generate a playlist with consistent moods between two or moreselections. The mood-based playlisting system 300 includes acommunications interface 310, a playlist manager 320, a content library330, a mood indicator library 340, a mood calculator 350, and anoptional mood-modeling engine 360. Generally, the mood base playlistingsystem 300 manages the playlist for one or more pieces of content to betransmitted to an audience. The communications interface 310 receivesdata describing the audience and one or more content goals to beincorporated, so that the playlist manager 320 may put together aplaylist of selections from the content library 330 by using the moodindicator library 340 to determine a score for the content andmaintaining consistency between the selected content using the moodcalculator 350.

The communications interface 310 may be used to exchange data describingthe audience that is being managed and/or to distribute playlistinformation. The communication interface 310 also may be used to receiveupdates to the content library 330, the mood indicator library 340, anddifferent algorithms and models used by the mood calculator 350.

The communications interface 310 receives updates from one or morepartners or other devices to exchange content for incorporation into aplaylist. For example, a newly-released song may be distributed, alongwith advertisements for incorporation into the playlist. Similarly, moodindicator information related to the content and/or advertising to bedistributed also may be received by the communications interface 310 fortransmission to the mood indicator library 340. Audience data associatedwith content may be modeled, described electronically, and transmittedto the mood calculator 350 to better select content to be incorporatedinto the playlist. The playlist manager 320 includes a code segment thatidentifies content to be used in a playlist. For example, the playlistmanager 320 may generate a playlist that describes a piece of content tobe accessed and reference information so that the content may beaccessed using the reference information.

Alternatively, the playlist manager 320 may generate a playlist to beused by a distribution point. For example, an Internet-based radiosystem may receive the playlist from the playlist manager fortransmission to the listening audience. Depending on the configurationof the mood-based playlisting system and whether the mood-basedplaylisting system is determining the playlist and distributing thecontent, the playlist manager 320 also may transmit the content to beused in the playlist (e.g., through communications interface 310).

The content library 330 may include one or more selections of contentfor incorporation into a transmission for a receiving audience.Depending on the nature of the content, the content library may beadjusted to accommodate the particular media and/or audio demands. Forexample, the content library may include digitally encoded songs andrelated music videos for broadband users. The content library also mayinclude metadata that describes the content. In the case of songs, themetadata may include, for example, artist, album, and track information.When the content library includes video information, the videoinformation may include different bit rates for different audiences.Thus, a user with a high bandwidth connection may be able to access aselection encoded for a higher bit rate and having relatively higherquality, while a user with a slower connection may be able to access thesame content encoded using a lower bit rate and having relatively lowerquality. The content library and the metadata in the content libraryalso may be associated with one or more rules that may be used in thecontent selection. Thus, a particular selection of content in thecontent library may have detailed licensing information that governs howthe selection of content may be accessed. For example, a particularselection of content may be available for promotional purposes during alimited time and may be unavailable thereafter. Other examples ofrestrictions that may be incorporated in the content library includeASCAP licensing restrictions that control the number of times aselection or content may be accessed in a particular period, andpreclude a selection of content from being accessed in a particularmanner. For example, a selection of content may be precluded from beingincorporated in a playlist twice in a row.

The mood indicator library 340 may include one or more values designedto describe the mood for a selection of content. Depending on theconfiguration of the mood-based playlisting system, different metricsmay be stored in the mood indicator library 340. Thus, one example ofthe value stored in the mood indicator library may describe a selectionof content and a mood indicator that scores the content in a specifiednumerical range. Another metric may include different values thatindicate whether a selection of content is compatible with a chosentheme or genre.

Although the mood-based playlisting system has been described asmaintaining consistency within a desired mood for a selection ofcontent, other non-mood-based elements may be modeled and incorporatedinto the content selection process and stored in the mood indicatorlibrary. For example, a user pool may be divided into premium andnon-premium communities. The premium community may be allowed to accessexclusive content that is not available to the non-premium community.This premium status for content that may be available may be stored inthe mood indicator library. Other non-mood-based metrics may be used.

For example, the mood indicator library 340 may include advertisingeffectiveness data. Examples advertising effectiveness data may include,but are not limited to, an indication of which advertisements should beused with specified moods, the effect of using an advertisement with thedifferent moods, projected and past advertisement efficacy (bothretaining a user and receiving a response) for both a user and ademographic, and reimbursement.

The mood indicator library 340 may be configured to incorporate feedbackbased on a user or a community of user's response to content. Forexample, the actual response by users to content may be tracked so thatefficacy data may be updated to reflect the users' actual responses.While a original data set may be used to predict a mood, the users'prior actions may be used in generating and consulting a mood model thatmore accurately predicts the users' response. Old rules that are notaccurate may be replaced by new rules determined to be more accurate.The new rules may be used to generate future playlists and/or selectcontent in the future for the user.

The mood calculator 350 may be used to receive values describing acurrent playlist, access the mood indicator library 340, and assist theplaylist manager 320 in generating the playlist. Depending on theconfiguration of the playlist manager 320, the structure of the moodcalculator 350 may differ. For example, in one configuration, theplaylist manager 320 may suggest a particular piece of content and pollthe mood calculator 350 to determine if the selection of content isappropriate and consistent with the current mood. The mood calculatorthen may respond with an indicator of whether the suggested content isconsistent.

Alternatively, the playlist manager 320 may provide an indicator of acurrent track that is being transmitted and may poll the mood calculator350 for a suggested piece of content. In response, the mood calculator350 may poll the mood indicator library 340 to retrieve a consistentpiece of content. The mood calculator 350 then may transmit the identityof the consistent content to the playlist manager 320, which mayretrieve the content from the content library 330.

As an optional element, the mood-based playlisting system 300 mayinclude a mood-modeling engine 360. For example, as content is beingadded to the content library 330, the mood-based playlisting system 300may interface with the mood-modeling engine 360 to determine and gaugethe mood spectrum for the newly-added content. The mood-modeling engine360 may use the communications interface 310 to develop an appropriatemood analytic for the newly added content. For example, the selectedcontent may be sent to a testing code segment to determine ananticipated user response. Alternatively, the mood-modeling engine 360may interface with the playlist manager to add the proposed content to atest group of listeners to gauge their response to the selected content.

Other analytics that may be used by the mood-modeling engine 360 mayinclude content analysis that may evaluate lyrics, the tempo, or otherelements relating to the content. In one example, the tempo for anewly-received piece of content may be “scored” using a frequencyanalyzer to determine the theme and mood with which the content isconsistent.

Although the mood-based playlisting system 300 is shown as aninterconnected group of sub-systems, the configuration of the mood-basedplaylisting system 300 may include elements that have allocated thefunctionality in a different manner. For example, the content library330 may be co-located or merged with the mood indicator library 340.Thus, the mood indicator for a selection of content may be stored as anelement of metadata residing with the content record. Alternatively, theelements described in mood-based playlisting system 300 may reside in alarger code segment with constituent code segments described by theelements shown in FIG. 3.

FIG. 4 is a flow chart 400 that illustrates how a track of content maybe selected in a way that maintains mood consistency. Specifically, theflow chart 400 may be implemented using the mood-based playlistingsystem such as was described previously. In general, a mood-basedplaylisting system determines a mood indicator that indicates a presentmood state of a user (step 410), determines a mood indicator describinga next track mood spectrum that is consistent with the mood indicatorfor the current track (step 420) and selects a next track that lieswithin the next track spectrum for the current track (step 430).

Initially, the mood-based playlisting system determines a mood indicatorthat indicates a present mood state of a user (step 410). Typically,this will include creating a score that describes the track of contentunder analysis. For example, a song being distributed on the radio couldbe given a score from 0 to 10. In a multi-dimensional scoring system,the mood indicator could include a multi-dimensional coordinate thatdescribes the mood indicator with regard to several variables.

The mood indicator may be determined in advance of distributing thetrack. For example, the system may assemble a user playlist with asequence of tracks for distribution. This sequence may be distributed todistribution nodes (e.g., local radio stations or regional Internetservers). Alternatively, the mood indicator may be determined for atrack that is being or has been distributed. For example, the moodindicator may be determined for a song that is being played over theairwaves.

A mood spectrum may be determined for the track for which a moodindicator has just been determined (step 420). The mood spectrum may beused to select the next track such that the next track lies within theboundaries of the mood spectrum. As has been described previously, themood spectrum may include multiple variables and may relate to alikelihood of success that a user may stay with the current distribution(e.g., the same channel) upon the playing of the next content selection.

With the mood indicator and the mood spectrum for the current trackdetermined, a next track is selected that lies within the mood spectrum(step 430). In one implementation, the next track may be selected byfinding the track that is closest to the current track. For example, ifthe current track has a score of 5.17, the next closest track that maybe selected may have a score of 5.18.

Alternatively, a content programmer may wish to have some variationwithin a mood spectrum, and the selection criteria may include arequirement that the next song differ by more than a specified variationthreshold while still being within the specified mood spectrum. In theprevious example, the content could be selected to be at least 0.5 unitsaway from the current selection of 5.17 but still lies within thevariation describing the mood spectrum of 1.0.

Within the range of values that are acceptable, the content may beselected randomly or the content may be selected based on identifyingcontent that matches the criteria (e.g., is the furthest or closest awaywithin the spectrum). If there is not a track that lies within the moodspectrum, the mood-based playlisting system 300 may alter itsconfiguration to generate a selection of content. For example, the moodspectrum may be expanded so that more content lies within the moodspectrum. This may be accomplished by, for example, decreasing thethreshold percentage of a success that is required in the transition orincreasing the values that define the threshold for success. Forexample, if the mood spectrum only covered 70's rock, the mood spectrummay be expanded to include 70's and 80's rock.

FIG. 5 illustrates a flow chart 500 showing a mood-based playlistingsystem that incorporates a three-dimensional mood-based modeling system.In general, the three-dimensional mood-based playlisting system operatesby determining a coordinate mood location for a current track that isbeing played. This may include or be described as the present mood stateof a user. With the coordinate mood location determined, a compatiblemood volume may be determined that describes future content selectionsthat are deemed consistent with the present mood state. With thecompatible mood volume identified, one or more tracks that lie withinthe compatible mood volume may be identified and a user may be able toaccess the identified tracks.

Initially, a coordinate mood location that indicates the present moodstate of a content selection is determined (step 510). For example, themood state may be described on X, Y and Z axes. In one example, thecoordinate mood location is described in the context of the content thatis being distributed. For example, the mood coordinates may measure thesongs lyrics, tempo, and/or style. Alternatively, the coordinate moodlocation may also measure or describe the mood of the audience. Forexample, a particular song may be associated with a human emotion suchas sadness, joy, excitement, or happiness. These human emotions may bemeasured independent of the underlying theme of the music. For example,some music whose theme is described as “golden oldies” may be associatedwith sadness while other music may be associated with joy.

With the coordinate mood location determined, a compatible mood volumedescribing compatible and consistent future content may be determined(step 520). For example, a sphere around a coordinate mood location maybe identified that describes content compatible with the present track.With the compatible mood volume described, one or more tracks that liewithin the mood volume may be identified (step 530). With the trackidentified, a user may be enabled to access the identified track (step540).

In FIG. 6, flow chart 600 illustrates how an audience may betransitioned from an originating piece of content to a destination pieceof content. This may be used, for example, to transition a user from aparticular piece of programming (i.e., the originating content) to atargeted advertisement (i.e., the destination content) by tailoring thetransitions from the originating content to the destination content.Accordingly, the likelihood of success and the effectiveness of thetransition may be pursued.

Generally, the operations described in flow chart 600 may be performedusing the systems and models described with respect to FIGS. 1-3. Forexample, the mood-based playlisting system 300 may be used to generatethe playlist that transitions the user from the originating piece ofcontent to the destination. Similarly, the transition and intermediatetracks described in FIG. 2D may be used to increase the effectiveness ofthe transitions. However, depending on the characteristics of theoriginating and destination content, the selection of the mood-basedtransition path may differ.

Generally, a mood-based playlisting system identifies a mood destinationfor a user playlist. A mood originating point is determined. With theoriginating point and destination paths known, a mood transition may becalculated from the mood originating point to the mood destination.

Initially, a mood destination for a user playlist is identified (step610). Generally, identifying a mood destination includes identifying aselection of content to be included in the user playlist. For example, adistributor may wish to place a certain advertisement. Alternatively, asystem administrator may wish to have an optimal lead-in for aparticular piece of programming for the purpose of, for example,achieving optimal ratings for network content. This content to beinserted in the user playlist has an associated mood that relates to thecontent being distributed. In yet another example, a systemadministrator for a mood-based playlisting system may wish to have anoptimal lead-in to increase the effectiveness and response of theaudience to identified content that is to be transmitted in the future.

Separately or in parallel, a mood originating point may be determined(step 620). Determining a mood originating point may include identifyingcontent that is being distributed or will be distributed to an audienceprior to the transmission of the content associated with the mooddestination. A mood originating point may be determined for the contentthat is being distributed. If the mood originating point differs fromthe mood destination of the content being transmitted (or to betransmitted), the resulting differential may create a mood transitionthat may create a less responsive result due to differences in the moodsof the particular content. The mood transition from the mood originatingpoint to the mood destination is calculated (step 630). Depending on thevariation between the mood destination and the mood originating point,the transition may include one or more intermediary tracks. Theintermediary tracks may be selected so that the mood metric for theintermediary tracks lies within the mood-consistency spectrum or volumeof the previous track. Using the previous content or track as abaseline, the next content or track may be selected to minimize thenumber of intermediary tracks between the originating content and thedestination content.

FIG. 7 is an exemplary GUI 700 that may be presented to a user accessinga mood-based playlisting system. For convenience, particular componentsand messaging formats described earlier are referenced as performing theprocess. However, similar methodologies may be applied in otherimplementations where different components are used to define thestructure of the system, or where the functionality is distributeddifferently among the components shown.

GUI 700 includes track and media player controls 710, a mood indicator720, a mood preservation control 730, a mood correction control 740, andan advertisement display 750.

The track and media player controls 710 display the current and nexttracks, and enable the user to control a media player (e.g., volumecontrols, rewind or fast forward, advance to a next track). Track andmedia player controls 710 indicate that the current track is EltonJohn's Philadelphia Freedom and the next track is the “Theme fromRocky.”

The optional mood indicator 720 features a display that informs a useras to their mood state/status as determined by with respect to amood-based playlisting system. The mood indicator 720 greets the user byscreen name (“Hello SCREEN_NAME”), indicates the mood state (in thiscase that the user is determined to be depressed), and also indicatesthe mood destination (that the proposed playlist is desired to upliftthe user). The screen name may be used to personalize the mood-basedplaylisting system to a user's identity. For example, a furrowed browfor a first user may be associated with an upset mood while a similarlyfurrowed brow for a second user may be associated with a neutral or evenpleased mood. Although the analysis of moods or facial expressions maybe personalized, a mood-based playlisting system may use normativeanalysis that is believed to be valid for large portions of thepopulation. The normative analysis may be enhanced by analyzing multipleportions of a facial expression so that uncommon facialexpressions/moods may be accounted for.

The optional mood preservation control 730 enables a user to preserve apresent mood. In the example shown, the user may click on a “I want tostay depressed” button to select content consistent with the depressedmood.

The mood correction control 740 enables a user to specify that theiractual mood is different from a mood that has been attributed to them.For example, if a user is actually happy while listening to the currenttrack (“Philadelphia Freedom”), the user may access a drop down menu(not shown) and indicate their actual mood, which in this case is happy.As shown, the mood correction control 740 renders an image of the user.

Activating the mood correction controls may enable a user to identify,select, or highlight one or more features in a facial expression. Forexample, in response to activating a drop down menu to indicate the useris happy, the media player may present the image (e.g., in moodconnection control 740) and ask the user to select one or more featuresassociated with a happy mood. The user then may interact with the imageand select a facial structure, such as a brow or smile, to indicate thatdetection of the selected facial structure reveals a particular mood.The indicated facial structure then may be stored and used in futureanalysis to identify a user's mood.

An optional image may appear in mood correction control 740 enabling auser to perceive a visual display. Presenting the visual display may beused in training the mood-based playlisting system to be responsive tothe actual mood of the user, or to illustrate to a user whether one ormore moods (e.g., a predicted mood) represents an actual mood.

The optional advertisement display 750 is used to present an image, oran audio/video clip. In the example shown, the optional advertisementdisplay 750 features a video advertisement for an energy drink withathletic images. In one implementation, the video clip is played whilethe audio content selection is playing. In another example, the videoclip is played upon the completion of the audio selection. Note that theadvertisement may be coupled to the mood, as indicated in GUI 700 wherean athletic advertisement is linked to an uplifting mood.

FIGS. 8, 9, and 10 illustrate exemplary GUIs 800, 900, and 1000 that maybe presented to a user while in training a mood recognition engine usedin a mood-based playlisting system. Although mood recognition engine maybe able to adequately recognize a mood for a particular user, a trainingprogram may be used to make the mood recognition engine more accurate,account for a nonstandard facial expression/mood indication, and/or toidentify which components in a facial expression should be used todetermine a mood state. Generally, the exemplary training program shownin GUIs 800, 900, and 1000 prompts a user to present a neutral, happy,and angry mood, as specified by mood prompter 810, 910, and 1010,respectively. Feedback displays 820, 920, and 1020 present an image ofthe user rendering the desired mood. In feedback display 820, a user'sneutral mood may be determined by detecting that (1) the brow structureand lips are parallel to the user's shoulder plane of the (parallel tothe horizon); (2) the lack of wrinkles around the mouth; (3) the neutralposition of the eyelid; and (4) the lack of tension present in thecheeks. In contrast, feedback display 920 illustrates how the presenceof the user's happy mood may be determined by detecting (1) the presenceof teeth, elliptical structure of the mouth, and pronounced angularstructure between the mouth and the nose indicate a smile, and thus, ahappy mood state; (2) the eyelids are retracted; and (3) the browstructure is curved around the eye. Finally, feedback display 1020illustrates how the presence of the user's angry mood may be determinedby detecting (1) the declining dip at the peripheral of the mouth; (2)the tension of muscles in the forehead and the cheek; (3) theorientation of the eyelid over the eye; and (4) the downward structureof the interior portion of the brow over the nose. Note that FIGS. 8-10illustrate how both frontal and portrait (from the side) images may beused to determine the actual mood.

The pronouncement of the features used to identify a mood may varybetween users. For example, in some users, anger may not be determinedunless pronounced wrinkles are detected in the forehead, while in otherusers (e.g., the user shown in FIG. 10) minimal tension may be used todetermine anger. Also, FIGS. 9 and 10 illustrate that even when a commonfeature is present in more than one mood (e.g., the presence of thepronounced angular structure between the peripheral of the mouth and thenose), other facial features may be used to infer mood. In response toviewing the feedback display, a user may alter a facial expression topresent a mood likely to be responsive and/or likely to resemble anactual mood.

The user may provide additional mood determination information usingalternate sensor gateways 830, 930, and 1030. The alternate sensorgateway allows a user to enter a heart rate, voice print and/or brainwave during the mood training process so that the heart rate, voiceprint, and brain wave metrics may be used in the future to betterdetermine a mood.

Although FIGS. 8-10 described a user actively training a moodrecognition engine, a mood-based playlisting system may use passivetraining techniques and/or more subtle training techniques. Toillustrate a passive training system, a mood-based playlisting systemmay play a content selection, and analyze the responsive facialexpressions as a baseline indicative of a particular mood. In a slightlymore active training system, a content selection is played withoutasking the user to present a particular expression during which an imageof the user is captured. In response to the content selection, and/orperceiving their image, the user is prompted to indicate a mood duringthe content selection. The mood-based playlisting system then may askthe user if the image is indicative of the mood provided by the user.Moreover, if the image is not indicative of expressed mood, the user mayadvance through a series of images captured during the rendering of thecontent selection to identify an image associated with the indicatedmood. Mother example used separately or addition to previously describedexamples allows a user to identify one or more components in a facialexpression indicative of the desired mood (e.g., by allowing the user tohighlight a brow structure, a lip structure such as a smile, or anexistence of wrinkles in a particular portion).

FIG. 11 is an exemplary listing of variables 1100 illustrating howfacial components may be used to generate a mood. Although listing 1100relates to an exemplary configuration variables, the listing 1100 alsomay be presented in a GUI enabling a user to identify which componentsmay be used in a mood-based playlisting system.

Listing 1100 includes hair-related descriptors including a position ofhairline 1110, a presence of hand on the hair 1111, and a presence/typeof hat 1112. Examples of using the position of the hairline 1110 mayindicate an incredulous/surprised mood when the position of the hairlineis forward, a stressed/expressed expression when the hairline is pulledback, and an indeterminate mood when the hairline is in a neutralposition. The presence of a hand on the head 1111 may indicate astressed mood (e.g., pulling hair out in frustration), tired (e.g.,running two hands through the hair), or ‘cool’ (e.g., running one handthrough the hair) A presence/type of hat indicator 1112 may indicate anathletic mood when the hat is a baseball cap (e.g., a upbeat, excited),cockiness/arrogance (e.g., wearing a hat backwards), or formality (e.g.,a Sunday bonnet).

The presence/existence/position of wrinkles 1120 may indicate moodand/or state, for example, through the indication of pain when wrinklesappear in the cheek, happiness when angled wrinkles appear around asmile, skepticism/bewilderment when wrinkles appear in the forehead, andunease when concentric wrinkles appear around the mouth.

The presence/position of facial muscles and/or of tension in the muscles1130 may be used to indicate intensity or determination when the lateralfacial or forehead muscles are tense, or relaxedness/contentment whenthe muscles are not being used.

The orientation of the eye structure 1131 may indicate unease/skepticismwith a squint, shock or unbelief with an open eye structure, anger withan slight squint, and a neutral or content mood with a normal eyeorientation. Eye structure may be determined by identifying the relativepositioning between constituent components (e.g., eyelash, eye line,eye-originating wrinkles, and/or the eye itself).

The brow 1140 may indicate anger/skepticism/dislike when furrowed,surprise when raised, and neutrality/happiness when raised. The angle ofa furrow may indicate an intensity and/or distinguish betweenanger/skepticism/dislike.

The mouth structure 1150 may indicate whether a user is responsive to acontent selection by mouthing the words, such as the case when thefacial structure changes at a frequency appearing in a contentselection. The angle of the lips and side of the mouth 1151 and thepresence of teeth 1152 may further refine/identify a mood. A smile mayindicate happiness, a frown may indicate unhappiness, yelling mayindicate anger, and clenched lips may indicate intensity or discomfort.

The presence and/or orientation of facial hair may be used to indicate amood, or used in conjunction with other components described previously.For example, a presence of muscles may be difficult to determine due tominimal variation in skin color or surface location. However, thetracking the movement of facial hair that mimics or corresponds to theunderlying component may be easier to detect given the texture inherentin many forms of facial hair.

In addition to using a component (e.g., a beard, a mustache, a style ofglasses, a hat, an earring, a piercing, and/or a tobacco product) toidentify a mood, the presence or absence of a component may be used toidentify a personality. For example, a goatee may be used to indicate aneasygoing personality with a preference for jazz, a full beard may beused to indicate a preference for country, sunglasses may be used toindicate a personality striving for a cool appearance, a set of bifocalsmay be used to indicate a preference for older or classical genres ofmusic, an earring or piercing may be used to indicate a preference forcontent on the fringes, a pipe may be used to indicate a preference forclassical music, and a cigar may be used to indicate a preference fortalk radio.

FIGS. 12A and 12B provide an exemplary user profile for SCREEN_NAME thatrelates a component in a facial expression to a mood that may be used ina mood-based playlisting system. The mood/facial components aredescribed in a programming construct that may appear in a configurationfile or that may be used as a script in a programming construct.

Rule 1210 indicates how a position of a hairline may be used todetermine a mood. In the example shown, a default rule indicates that aforward hairline indicates a relaxed mood. When the hairline is setback, a tense mood is inferred which includes the set of anger, anxiety,upset and upset moods. Rule 1211 indicates how the presence of a hand onhair includes a default rule where SCREEN_NAME is deemed tense when ahand lies on the hair. Similarly, rule 1212 indicates that when abaseball cap is being used, then a mood is believed to be relaxed orhappy.

Rule 1220 uses a presence and/or position of wrinkles to determine moodinformation. If wrinkles exist in the forehead, then the mood isdetermined to include a tense set that includes anger, anxiety, and/orupset moods. If vertical wrinkles exist outside a structure identifiedas a mouth, then the mood is determined to be anger. If verticalwrinkles exist outside of the eye, then the mood is determined toinclude pain.

Rule 1230 uses the presence and/or position of facial features (e.g.,facial muscles) and/or of tension in the muscles to determine moodinformation. For example, if the cheeks are believed to be tense, thenthe mood may be determined to include business moods which includes theset of anxiety and focus moods. In contrast, when the cheeks arebelieved to be relaxed, the mood may be described as NOT business andNOT tense moods (e.g., any or all moods except the moods found inbusiness and tense mood sets).

Rule 1231 indicates that the orientation of eye structure may be used todetermine a mood. In particular, if an eye is squinted, the mood may bedeemed nonresponsive, which includes skeptical, dislike, and/or painmoods. If the eye is closed, the mood is determined to include slowmoods which includes sleep, bored, and/or relaxed moods. If the eyes arebelieved to be wide opened, then the moods are determined to includewonder moods that include the moods of shock, amaze, and/or curiosity.

Rule 1240 indicates how brow information may be used in determining amood. For example, brow information may include a presence offurrowed/unfurrowed brow structures and/or relate to positioninformation. As shown, a furrowed brow is described as a downward browor a brow with an angle less than 135 degrees.

If a brow is furrowed, then the mood is determined to include a tensemood. In contrast, an unfurrowed brow is defined as an upward brow or abrow at an angle of less than 135 degrees. If the brow is unfurrowed,then the mood is determined to include a relaxed or happy mood.

Rule 1250 describes how the presence/position of a mouth structure maybe used to determine mood information. Rule 1251 indicates that theangle of lips and side of mouth may be used in determining a mood. Rule1252 indicates that the presence of teeth in an image can be used toidentify a mood. A smile is determined to exist when the end of themouth is pointed up or teeth are determined to be together. If a smileis identified, then a mood is determined to include relaxed and happymoods. However, if teeth are identified, but the teeth are not togetherand a microphone detects yelling, the mood is determined to includeangry moods.

As discussed earlier, rule 1260 indicates how the presence ororientation of hair facial hair may be used to determine a mood. Whendreadlocks exist, the a mood is determined to be reggae. If the user isunshaven, a mood may not include classical or pop. If a full beard isdetected, then the mood may include rock, metal, or country music.

Rule 1270 indicates miscellaneous factors that may be used to indicate amood. For example, when a pipe is detected, the mood may includerelaxed, classical, jazz, and national public radio states.

Rule 1280 prioritizes between different moods. Depending on how theunderlying mood is modeled, elements of different moods may be detected.Prioritization provides rules that resolve competing, inconsistent, ordiffering mood states. For example, a business mood may be favored overa happy mood. A nonresponsive mood may be favored over a tense mood. Ifchildren are detected (e.g., background audio signals are identified asscreaming or yelling) using the camera or microphone, then no metalmusic may be played.

Mood conflicts may be resolved using a number of different models. Inone model, a dominant mood is identified and content responsive to thedominant mood is selected in response. For instance, imagery provided bya camera may include a smile indicating happiness, strained facialmuscles indicating tension, and a set back hair line also indicatingtension. In one implementation of the dominant mood model, tension isidentified as a dominant mood since the majority of the detected moodsindicate the user is experiencing tension. In another variation, tensionmay be defined as a dominant mood over happiness by virtue a rule thatspecifies that tension should be used as the mood even when happiness isdetected. Identifying the dominant mood may include using a ranked listof moods, or a collection of relative mood preferences. Yet anothervariation may include deriving metrics for the magnitude of any one moodstate and comparing the metrics associated with the mood. Thus if a userhas a larger smile or maintains a smile over a longer duration whilemomentarily presenting a tense appearance, the happiness associated withthe smile of the longer duration may be identified as the dominant mood.

In another model, the mood-based playlisting system may select one ofseveral identified moods to accomplish the objective. For instance, ifthe mood-based playlisting system determines that a user may beexperiencing tiredness, happiness, and shock, the mood-based playlistingsystem may attempt to work with the happiness mood to realizeobjectives. When a user is nonresponsive to content oriented towards thehappiness mood, another mood may be used.

In one implementation, several models for resolving conflicts may beused. For example, a hybrid of models may be used so that, ifassimilating multiple models indicates a prevailing mood state, suggestsa particular transition, or identifies a particular content selection,the indicated state, transition, or selection may be used. Separately orin addition, if user responsiveness indicates that one model is moreeffective than another model, the more effective model may be used forthose configurations and environments for which the more effective modelis deemed effective. When configuration and environmental data indicatesthat another model is more effective, then the other model may be used.

Rule 1290 allows a mood state to be more customized, that is, moreprecisely tailored to associate content with a mood. The customizationmay be applied to a subscriber community, a listening audienceassociated with an Internet radio station, a demographic, or user. Forexample, rock has been modified to exclude ARTIST1 and ARTIST2. A talkmood has been modified to exclude TALK_SHOW_HOST_(—)1. Happy includesMETAL and ROCK and does not include POP unless ARTIST3 is singing orSONG4 is provided.

FIGS. 13A, 13B, and 13C illustrate an exemplary scripted sequence ofoperations 1300 for a mood-based playlisting system configured toprovide content using mood-based selection criteria based, relying inpart, on a camera to provide mood information. Generally, sequence 1300represents the operations that are performed and the results that arerealized using the mood-based playlisting system with camera inputs. Inone implementation, the sequence of operations 1300 represents actualentries appearing in a log used by a mood-based playlisting system. Inanother example, the sequence of operations 1300 represents a sequenceof procedural calls and resultant data.

Operation 1305 is a conditional function that specifies placement ofADVERTISEMENT′ in response to the condition that an emotional state, anintensity, a tempo, a genre lead to PLACEMENT_CRITERIA. For example,rule 1305 may be invoked in anticipation of or upon reaching of acommercial break at the end of a sequence of content selections.

Operation 1310 indicates that an advertisement for a sports drink shouldbe played when the mood is happy, intense, upbeat, and the song type(e.g., genre) is rock or hip hop. When the song type is rock, then arock advertisement should be placed. When the song type is hip hop, thena hip hop advertisement may be placed.

Operation 1315 provides a history for an online identity identified asSCREEN_NAME. The history may be used to understand or determine a user'sperceived mood state and/or recent transitions in mood. Thus, a user mayhave selected an 80's station, skipped the “The Lady in Red” and RunDMC, and listened to Van Haden in the preceding sequence. A preliminarymood determination is made using only the previous sequence. As aresult, operation 1320 predicts a mood state as anger, intense, upbeat,rock, not ballad (e.g., based on the user skipping the ballad “The Ladyin Red”), and not hip hop (e.g., based on the user skipping hip hopsongs by Run DMC). Thus, past perceived mood transitions may be used asa basis for future determination of how/whether/when to invoketransitions from their current mood to a desired mood. A log of pastperceived mood transitions may be recorded for a user (or community ofusers). The log may record a user's facial expressions captured duringthe course of the rendering content selections so that the perceivedmood transitions is based on imagery-based mood state determinations.

Operation 1325 specifies that the mood should be confirmed using acamera. In operation 1330, the camera indicates that the actual mood isanger, neutral, nonresponsive, and indeterminate.

Thus, as a result, the desired mood state may be related to the actualmood state. As shown in operation 1335, differences between the desiredmood state and the actual mood state may be used to indicate that theuser needs to transition from anger to happy, from neutral to intense,and from nonresponsive to upbeat. If the genre is ok, and the existingmood may be used.

In operation 1340, the mood indicator library is queried to identify acontent selection that supports the transition.

In response to the query, in operation 1345, the mood indicator library(e.g., mood indicator library 340 in FIG. 3) returns data indicatingthat if a rock advertisement is placed, 50% of users will listen throughthe end of commercial. The mood indicator library also indicates thatadvertisers will not pay for a 50% retention. If Huey Lewis is used asan intermediary track before the commercial, then the retentionlikelihood becomes 75%. On the other hand, if the intermediary trackincludes Bon Jovi's “Living on a Prayer”, the likelihood is 90%. Thecost per Huey Lewis is $0.0001 per listener, the cost per Bob Jovi is$0.0011. The reimbursement from the advertiser is 75% while thereimbursement at 90% is $0.003

As a result, Bon Jovi living on a prayer is selected in operation 1355.The predicted mood is happy, intense, upbeat, and rock. The differencesin cost compared to efficiency need not be the determinative factor. Forexample, some advertisers may go to extraordinary lengths to preservebrand awareness or perception (e.g., is the advertised product deem“cool”. One measure of brand awareness may include retention rates.Thus, an advertiser concerned about brand perception may pay a premiumto ensure the highest retention.

In operation 1360, imagery data is analyzed to determine the actualmood. For example, data from the camera indicates that the actual moodis happy, intense, nonresponsive, and rock. The predicted mood isrelated to the actual mood. The emotional state, tempo, and genre areacceptable. However, the intensity needs to change from nonresponsive toupbeat.

To change the intensity from nonresponsive to upbeat, the mood indicatorlibrary is queried to provide a content selection that supports the moodtransition. In operation 1370, the mood indicator library indicates that85% will listen to entire commercial if the rock advertisement is playednow, and that 95% will listen to the entire commercial if the GratefulDead's “Touch of Gray” is played. As a result, the advertiserreimbursement at 85% is $0.0025 while the reimbursement at 95% is$0.005. To realize the increased reimbursement, a “Touch of Gray” isplayed in operation 1375.

The camera is used confirm that the actual mood is in fact happy,intense, upbeat, and rock (in operation 1380), and that the rockadvertisement is played in operation 1385.

To confirm recipient response, the camera is used to generate imagerydata, which confirms that the actual mood during the rock advertisementwas happy, intense, upbeat, and rock. In operation 1390, the mediaplayer indicates that SCREEN_NAME listened to entire selection and thatthe user selected the advertisement, entitling advertiser to a bonus.

FIG. 14 is a flow chart of an exemplary process 1400 by which a user'smood variations may be recorded. Generally, the operations in process1400 may be used in conjunction with the systems and configuresdescribed elsewhere in the document. For example, at operation 1450, theunderlying mood models described with respect to FIGS. 2A-2D, 12, and 13may be used to determine a mood for the user and select contentresponsive to the mood. Moreover, the operations may be performed on theplayer 130 and/or the mood based playlisting system 300 described inFIGS. 1 and 3, respectively. For convenience, particular components andmessaging formats described earlier are referenced as performing theprocess. However, similar methodologies may be applied in otherimplementations where different components are used to define thestructure of the system, or where the functionality is distributeddifferently among the components shown.

Initially, a user is enrolled in a training regimen (1410). For example,a user may be presented with a content selection and asked to specify aresultant mood. In another example, a user is asked to present a faceassociated with different moods. One example of a training regimen isshown in FIGS. 8-10.

Once the mood-based playlisting system has been trained, a camera isused to capture an image of the user (1420), and the image is analyzed(1430). For example, the image may be analyzed using the rules andconfigures described in FIGS. 12 and 13. In addition, the predicted moodmay be optionally determined using non-camera inputs (1440). Forexample, the mood-based playlisting system may determine a mood based ona selected station or channel or by reference a past historicalreference for a user's mood.

The mood is determined for the user (1450), and content responsive tothe mood is selected (1460). A camera is used to capture an image of theuser during or while perceiving the content (1470), and the image isanalyzed to determine an actual mood state (1480).

The mood-based playlisting system determines whether a predicted moodvaries from the actual mood (1480). If so, the variation is recorded andused when selecting content in the future (1485). If not, the mood-basedplaylisting system may record that the predicted mood was an accuratepredictor of the actual mood (1490). For example, the variation oraccuracy may be provided to the mood indicator library 340 described inFIG. 3.

FIG. 15 is an exemplary GUI 1500 that illustrates how different inputsmay be used as a mood sensor into a mood-based playlisting system. Whiledata provided by the sensors may differ from other data provided byother sensors in the mood-based playlisting system, the data provided bythe sensors shown in UI 1500 may be associated with different moodstates and used in a mood-based playlisting system. Furthermore, whilesome sensors may use a different form factor that constrains where thesensors may be used, using different sensors in different circumstancesenables the mood-based playlisting system to realize feature setsdifficult to otherwise achieve. For example, interface 1510 illustratesneural activity in imagery provided by a brain scan. In someimplementations, the form factor of the radiology equipment may limitthe use of the radiology equipment to laboratory and medicalenvironments. However, the radiology equipment may provide a degreeaccuracy not easily obtained through other sensors. And, the dataprovided by the radiology equipment may be used to generate a moresophisticated model, which in turn may lead to more accurate results. Asshown, the interface 1510 indicates neural activity in a particular areaof the brain associated with a particular mood.

Interface 1520 illustrates data provided by an audio spectrum analyzer.Mood information may be derived by associating a certain frequency orarrangement of frequencies with a mood.

Interface 1530 illustrates data provided by an electronic signalmonitor. For example, synaptic activity indicative of a mood state maybe detected by a probe attached to the user. The relative intensity orfrequency may be used to indicate a mood state for the attached user. Inone implementation, a degree of tension may be determined and used togenerate the mood state.

Other implementations are within the scope of the following claims. Forexample, although the mood-based playlisting system has been describedin the context of a distributed system that may support multipledevices, the mood-based playlisting system may be distributed acrossmultiple systems and/or reside at a client device. One example of themood-based playlisting system being distributed across multiple devicesmay include having a portion of the mood-based playlisting system thatoperates in a data center where the content library and mood indicatorlibrary reside. These data center systems may interface with softwarethat operates a mood calculator and content retrieval program retrievethe content library from the central systems.

Alternatively, the mood-based playlisting system may be moreclient-focused and may perform more operations on the client. Forexample, the mood-based playlisting system described in FIG. 3 may beimplemented on a personal audio system. The personal audio system maystore multiple selections of content in memory and generate the playlistthat maintains the mood of the content that has been stored.Alternatively, the mood-based playlisting system may include anetwork-based device that implements the content selection andplaylisting on the client device but retrieves content from anetwork-based content library.

The mood-based playlisting system may be configured to preserve somemeasure of variation within the playlist. Thus, the mood-basedplaylisting system may be configured to recognize that if three countryballads having moods that have been gauged as depressing are played, theplaylist should then select a country song having a mood that has beengauged as uplifting. These variation rules may be described digitallyand distributed as programming criteria alongside or in conjunction withother licensing restrictions. For example, a license may govern thefrequency with which an artist or song may be played. In addition to thefrequency licensing restrictions, the content distributor may distributea mood-based playlisting rule set along with an electronic library toregulate access to the content.

Although the mood has been described in the context of content beingplayed, other techniques may be used to infer the mood. For example, theclient device may monitor how the user interfaces with the media player.Monitoring a volume level, monitoring changes to the volume level, andmonitoring whether a user changes an Internet radio station are examplesof operations that may be used to infer the mood. For example, when amedia player detects that a user reduces the volume level when a newtrack begins, the media player may determine that the user isexperiencing a less intense mood. In contrast, when the user increasesthe volume, the media player may determine that the user's moodintensifies.

The user interaction with the media player also may be analyzed withrespect to the content that is accessed. For example, if the user skipsto the next track immediately after accessing a new track, the mediaplayer may determine that the user's mood does not like the skippedtrack. The user's action may be extrapolated so that a mood that is theinverse of the mood of the rejected content is inferred. To illustrate,a user may initially select a country music Internet Radio station. Thesequence of content transmitted to the user may include a country rocksong, followed by a country ballad, followed by a country rock song.When the user listens to the first country rock song, skips the countryballad, and listens to the second country rock song, the media player(or host) may determine that the user's mood reflects a preference forcountry rock.

Although the description of a mood indication made distinctions betweenthe style and genre, the mood indications also may be made with respectto other factors, including the artist, the tempo, the era in which thecontent originated, the album, and/or other categorization. For otherforms of media (e.g., video or data), the mood indications may includemoods related to the identity of the producer, director, actors, and/orcontent rating (child, teen, all-ages) in addition to the category ofthe programming.

Analyzing the user's interactions to determine the mood is not limitedto the user's interaction with a media player. A user's interaction withan Instant Messaging program, an electronic mail program, or an InternetWeb browser are examples of other user activities that may be used todetermine the mood. Thus, when a user is typing quickly and exchangingmessages with many other users, an intense mood may be inferred. Incontrast, when the user is determined to be reading web pages at aslower pace, a relaxed mood may be inferred. The content in the userinteraction also may be used in determining the mood. Thus, the contentappearing in a web page accessed by the user may be used to determinethe mood for the user.

Although many of the previously described examples link a certainactivity or type of content with a certain mood, the examples illustratejust one mood that can be associated with an activity. Other moods maybe associated with the activity or type of content. A selection ofcontent or a user activity also may be associated with multiple moods.An example of content with multiple moods may include a song with anuplifting melody and depressing lyrics. A mood-based playlisting systemmay use either or both moods in selecting future content. If themood-based playlisting system sought to place an advertisement/productwith the uplifting mood indication, the mood-based playlisting systemmay incorporate the uplifting mood in the transition. If the mood-basedplaylisting system did not have an intended mood destination in mind,the mood-based playlisting system may continue to select content withmultiple mood elements to allow for an easier transition to a widervariety of content. A larger mood volume may represent the multipleelements with greater dimensions across multiple axes.

Although the mood-based playlisting system has been described usingplaylists, the mood-based playlisting system need not assemble an actualplaylist of songs. Rather, the content selection may be made on aselection-by-selection basis. The list of songs selected in this mannermay form a playlist.

Although the mood-based playlisting system has been described in thecontext of determining the mood state for a user, the mood-basedplaylisting system may be used to determine a mood state and selectcontent for a group of users. This may include selecting content forlarge Internet audiences. For example, the individual mood states forindividual members of a large audience may be aggregated to determine acollective mood state for the large audience.

In one example, the determining collective mood state may includesampling individual members of the audience for their mood states andusing the sampled mood information to generate a collective mood state.In another example, an audience listening to one content source may beanalyzed as a collection of groups. The mood-based playlisting systemmay analyze each individual group to determine whether the mood state ofthe group is consistent with the content being selected. When the moodstate for one of the groups indicates that the mood state for the groupis not compatible with the mood state for a content selection, themood-based playlisting system may reconfigure the selection of content.In one example, the group experiencing the mood state incompatibilitymay be transitioned to a different stream/playlist to preserve the moodstate compatibility. In another example, the mood-based playlistingsystem may select different content designed to retain the groupexperiencing the mood state incompatibility. This may includedetermining that more users are likely to be retained from the groupexperiencing the mood state incompatibility than are lost from othergroups not experiencing the mood state incompatibility.

The mood-based playlisting system may disperse and group users. Usersmay be grouped to reduce costs, to take advantage of discounts forlarger audiences, and to allow a limited pool of content to serve alarger community. This may include transmitting the same advertisementor segment lead to multiple users. The mood-based playlisting systemalso may disperse users from a common group. For example, a group ofusers may be accessing a host to access a popular selection of content.The mood-based playlisting system then may personalize the content basedon the determined mood so that the users are retained at a higher rateusing the mood-based playlisting system.

The mood-based playlisting system may normalize a mood indication to adesignated location or region. The normalization may be doneirrespective of whether targeted content is designated for the user. Forexample, the mood-based playlisting system may determine that operatinga playlist in a certain mood spectrum or volume retains listeners at agreater rate. In another example, the mood indication for the user isoperated in a specified range so that the user may be more receptive tocommunications delivered through other channels. This may include, forexample, an advertisement on television, an electronic mail message, atelephone call, a Web-based advertisement, or an instant message. In yetanother example, an advertiser may want a certain mood to be associatedwith a product. For example, a marketing firm may want a ‘happy’ moodassociated with the firm's content.

When calculating a mood transition, the mood-based playlisting systemmay reexamine the actual mood state during the transition and determineif the actual mood state matches the intended mood state. For example,although the mood state of the content may indicate that a user shouldbe in a relaxed mood, the user's activities on their client may indicatethat the user's mood state is not mellow (e.g., the user is experiencingstress or anxiety). The mood-based playlisting system may dynamicallyrespond to the actual mood state. In one example, the mood-basedplaylisting system may select content associated with a different mooddestination that is more compatible with the user's actual mood state.Thus, instead of playing an advertisement associated with a mellow mood,the mood-based playlisting system may select an advertisement with amood that is compatible with the actual mood of the user.

The mood based-playlisting system may include a detailed records systemfor reporting and accounting. For example, the mood-based playlistingsystem may record the moods of the user, the mood transition betweentracks, and the percentage of users that are retained for thetransition. Other records may include advertising effectiveness based onthe mood, and user listening habits (e.g., duration, user preferences).The records may be refined in an automated manner to develop moodtrending information. The mood-based playlisting system may generateautomated reports for system administrators and advertisers to improvethe enjoyment, effectiveness, and/or success of the mood-basedplaylisting system. This may include a report indicating that adifferent programming sequence may result in an increased response rateto an advertisement.

The mood-based reporting system may transmit several different sequencesof content to determine the relative efficacy of the differentsequences. The mood-based reporting system then may present the resultsto a system administrator and enable the system administrator to controlfuture content selection using the reported relative efficacyinformation. The reporting system may present results using differentmood metrics. For example, a first report may be based on only the moodof the content while a second report may gauge the user interaction withthe media player. The reporting system then may analyze the differences,and interpret the variation. The interpretation of the variation thenmay be used by a system administrator to plan future programming.

In another implementation, a computer program may be configured tomanage content accessed by a user by using a mood-based playlistingsystem to plot a transition from a first track to a second track usingat least one intermediary track. A mood originating point may bedetermined for the first track, and an intermediary track configured torealize a specified mood transition may be selected. The intermediarytrack may be rendered and a camera may be used to capture an image ofthe user during the rendering of the content. The image may be analyzedto realize an actual mood state, and the actual mood state may be usedto determine if the specified mood transition has been realized. Whenthe specified mood transition has been realized, the transition mayproceed. When the specified mood transition has not been realized, theactual mood may be used to select a different transition track.

In still another variation, a computer program may be configured tomanage electronic content made available to users. A first contentselection may be rendered, and a camera may be used to capture an imageof the user during rendering. The image may be analyzed to determine anactual mood indicator for the user. A mood spectrum may be determined todescribe other mood indicators that are consistent with the actual mood.Finally, a next content selection may be selected having a second moodindicator that lies within the mood spectrum.

What is claimed is:
 1. A method for providing mood-based contentdelivery, the method comprising the following operations performed byone or more processors: determining a present mood of a user; providing,to the user, digital media content to change a mood of the user from thedetermined present mood to a destination mood; providing, on a display,an indication of the determined present mood and the destination mood;providing, on the display, a mood correction control that enables theuser to specify a mood of the user; and modifying the digital mediacontent in response to the mood specified by the user through the moodcorrection control, the specified mood being different from thedetermined present mood.
 2. The method of claim 1, further comprisinganalyzing an image of the user to determine the present mood of theuser.
 3. The method of claim 2, wherein analyzing the image of the userto determine the present mood of the user includes identifying a changein a location of a facial feature of the user, and using the change inlocation to determine the present mood of the user.
 4. The method ofclaim 3, wherein the facial feature includes a brow, a wrinkle, adimple, a hairline, an ear, a nose, a mouth, or a chin.
 5. The method ofclaim 1, further comprising analyzing a mood rating of the digital mediacontent provided to the user to determine the present mood of the user.6. The method of claim 1, further comprising: presenting an image of theuser on the display in response to the mood specified by the user; andreceiving, in response to presenting the image, a selection from theuser corresponding to one or more facial structure features that areassociated with the specified mood of the user.
 7. The method of claim1, further comprising displaying, on the display, an advertisementassociated with the destination mood.
 8. A non-transitorycomputer-readable storage medium storing instructions that areexecutable by at least one processor to cause the at least one processorto execute a method, the method comprising: determining a present moodof a user; providing, to the user, digital media content to change amood of the user from the determined present mood to a destination mood;providing, on a display, an indication of the determined present moodand the destination mood; providing, on the display, a mood correctioncontrol that enables the user to specify a mood of the user; andmodifying the digital media content in response to the mood specified bythe user through the mood correction control, the specified mood beingdifferent from the determined present mood.
 9. The non-transitorycomputer-readable storage medium of claim 8, wherein the method furthercomprises analyzing an image of the user to determine the present moodof the user.
 10. The non-transitory computer-readable storage medium ofclaim 9, wherein analyzing the image of the user to determine thepresent mood of the user includes identifying a change in a location ofa facial feature of the user, and using the change in location todetermine the present mood of the user.
 11. The non-transitorycomputer-readable storage medium of claim 10, wherein the facial featureincludes a brow, a wrinkle, a dimple, a hairline, an ear, a nose, amouth, or a chin.
 12. The non-transitory computer-readable storagemedium of claim 8, wherein the method further comprises analyzing a moodrating of the digital media content provided to the user to determinethe present mood of the user.
 13. The non-transitory computer-readablestorage medium of claim 8, wherein the method further comprises:presenting an image of the user on the display in response to the moodspecified by the user; and receiving, in response to presenting theimage, a selection from the user corresponding to one or more facialstructure features that are associated with the specified mood of theuser.
 14. The non-transitory computer-readable storage medium of claim8, wherein the method further comprises displaying, on the display, anadvertisement associated with the destination mood.
 15. An electronicapparatus, comprising: at least one processor; and a memory device thatstores instructions, wherein the at least one processor executes theinstructions to: determine a present mood of a user; provide, to theuser, digital media content to change a mood of the user from thedetermined present mood to a destination mood; provide, on a display, anindication of the determined present mood and the destination mood;provide, on the display, a mood correction control that enables the userto specify a mood of the user; and modify the digital media content inresponse to the mood specified by the user through the mood correctioncontrol, the specified mood being different from the determined presentmood.
 16. The electronic apparatus of claim 15, wherein the at least oneprocessor further executes the instructions to analyze an image of theuser to determine the present mood of the user.
 17. The electronicapparatus of claim 16, wherein analyzing the image of the user todetermine the present mood of the user includes identifying a change ina location of a facial feature of the user, and using the change inlocation to determine the present mood of the user.
 18. The electronicapparatus of claim 17, wherein the facial feature includes a brow, awrinkle, a dimple, a hairline, an ear, a nose, a mouth, or a chin. 19.The electronic apparatus of claim 15, wherein the at least one processorfurther executes the instructions to analyze a mood rating of thedigital media content provided to the user to determine the present moodof the user.
 20. The electronic apparatus of claim 15, wherein the atleast one processor further executes the instructions to display, on thedisplay, an advertisement associated with the destination mood.